Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers

Christophe Cruz 1 Cyril Nguyen Van Laurent Gautier 2
1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
2 MSH Dijon (MSHD) - Maison des Sciences de l'Homme de Dijon
Abstract : This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the two communities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, by approach the similarity between words or by revealing hidden semantic relations. Thus, these controlled vocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process.
Keywords : Recommander systems Wine Ontology Word embedding Semantics Terminology Expert Discourse
Type de document :
Article dans une revue
Open Journal of Web Technologies, RonPub, 2018, Special Issue: Proceedings of the International Workshop on Web Data Processing & Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence, 5 (1), pp.23-30. 〈https://www.ronpub.com/ojwt/OJWT_2018v5i1n04_Cruz.html〉
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https://halshs.archives-ouvertes.fr/halshs-01872273
Contributeur : Laurent Gautier <>
Soumis le : mardi 11 septembre 2018 - 18:42:38
Dernière modification le : jeudi 7 février 2019 - 15:45:09
Document(s) archivé(s) le : mercredi 12 décembre 2018 - 15:51:09

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Christophe Cruz, Cyril Nguyen Van, Laurent Gautier. Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers. Open Journal of Web Technologies, RonPub, 2018, Special Issue: Proceedings of the International Workshop on Web Data Processing & Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence, 5 (1), pp.23-30. 〈https://www.ronpub.com/ojwt/OJWT_2018v5i1n04_Cruz.html〉. 〈halshs-01872273〉

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